ABSTRACT

Empirical evidence shows that a stock’s high and low prices are temporary, and stock price relatives are likely to follow the mean reversion phenomenon. While existing mean reversion strategies can achieve good empirical performance on many real datasets, they often make a single-period mean reversion assumption, which is not always satisfied, leading to poor performance on some real datasets. To overcome the limitation, this chapter (Li et al. 2015) proposes a multiple-period mean reversion, or the so-called moving average reversion (MAR), and a new online portfolio selection (OLPS) strategy named the online moving average reversion (OLMAR), which exploits MAR by applying powerful online learning techniques. Our empirical evaluations in Part IV show that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to superior performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications.